• Journal of applied mathematics & informatics
• /
• v.38 no.5_6
• /
• pp.519-532
• /
• 2020

In this paper, we first propose a new total variation-based regularization model for image restoration. We next propose a fixed-point-like method for solving the new image restoration model, and then we provide convergence analysis for the fixed-point-like method. To evaluate the feasibility and efficiency of the fixed-point-like method for the new proposed total variation-based regularization model, we provide numerical experiments for several test problems.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.4
• /
• pp.185-195
• /
• 2020

The study aims to assess the impact of destination image, satisfaction and loyalty of tourists at mountain destinations in Thanh Hoa province, Vietnam. The study involves questionnaire surveys and multivariate data analysis methods (Cronbach Alpha test, EFA, CFA, SEM). Research results from 500 tourists in the mountain destinations of Thanh Hoa province demonstrate that all factors have imposed a positive impact on tourist satisfaction, specifically: The most influential factor is Natural features, followed by Human factors while the least influential factor is Infrastructure; On the other hand, research results also demonstrate that satisfaction has a substantial impact on tourist loyalty. Based on the research results, we also proposed some key solutions to enhance the destination image, thereby contributing to increased satisfaction and loyalty of tourists, including: (i) Promoting Natural Tourism Resources. (ii) Raising Awareness of Environmental Protection. (iii) Building Local Cultural Identity. (iv) Building Exclusive Tourist Products. (5) Strengthening the Support of Local Authorities for Tourism Activities. (vi) Developing a Price Policy.

• Journal of the Korean Society of Industry Convergence
• /
• v.13 no.4
• /
• pp.227-233
• /
• 2010

This study analyzes the images of city residents on the landscape elements of the rural theme villages. The results of analysis bring the following conclusions. 1) Important factor of rural landscape with worth preserving is natural landscape such as dense forests, trees and creek. 2) Natural landscape such as forests and trees is evaluated high in image-assessment as well. However, it is evaluated low in the image of "Diversity", therefore, various species of trees need to be preserved. 3) In the future, people who spent their life only in the city would be the main stream of Green-tourism, and their structure of image-assessment needs to be reorganized.

• The Journal of Natural Sciences
• /
• v.8 no.2
• /
• pp.77-82
• /
• 1996

New very low bit rate segmentation image coding technique is proposed by segmenting image into textually homogeneous regions. Regions are classified into on of three perceptually distinct texture classes (perceived constant intensity (class I), smooth texture (class II), and rough texture (class III) using the human Visual System (HVS) and the fractals. To design very low bit rate image coder, it is very important to determine nonoverlap and overlap segmentation method for each texture class. Good quality reconstructed images are obtained with about 0.10 to 0.21 bit per pixel (bpp) for many different types of imagery.

• Electronics and Telecommunications Trends
• /
• v.39 no.1
• /
• pp.95-105
• /
• 2024

In natural language processing, large language models such as GPT-4 have recently been in the spotlight. The performance of natural language processing has advanced dramatically driven by an increase in the number of model parameters related to the number of acceptable input tokens and model size. Research on multimodal models that can simultaneously process natural language and image data is being actively conducted. Moreover, natural-language and image-based reasoning capabilities of large language models is being explored in robot artificial intelligence technology. We discuss research and related patent trends in robot task planning and code generation for robot control using large language models.

• Journal of the Korean Society of Radiology
• /
• v.13 no.4
• /
• pp.623-628
• /
• 2019

The aim of this study was to design fast non local means (FNLM) noise reduction algorithm and to confirm its application feasibility in light microscopic image. For that aim, we acquired mouse first molar image and compared between previous widely used noise reduction algorithm and our proposed FNLM algorithm in acquired light microscopic image. Contrast to noise ratio, coefficient of variation, and no reference-based evaluation parameter such as natural image quality evaluator (NIQE) and blind/referenceless image spatial quality evaluator (BRISQUE) were used in this study. According to the result, our proposed FNLM noise reduction algorithm can achieve excellent result in all evaluation parameters. In particular, it was confirmed that the NIQE and BRISQUE evaluation parameters for analyzing the overall morphologcal image of the tooth were 1.14 and 1.12 times better than the original image, respectively. In conclusion, we demonstrated the usefulness and feasibility of FNLM noise reduction algorithm in light microscopic image of small animal tooth.

• Smart Media Journal
• /
• v.10 no.1
• /
• pp.63-69
• /
• 2021

When a person sees a sentence and understands the sentence, the person understands the sentence by reminiscent of the main word in the sentence as an image. Text-to-image is what allows computers to do this associative process. The previous deep learning-based text-to-image model extracts text features using Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM) and bi-directional LSTM, and generates an image by inputting it to the GAN. The previous text-to-image model uses basic embedding in text feature extraction, and it takes a long time to train because images are generated using several modules. Therefore, in this research, we propose a method of extracting features by using the attention mechanism, which has improved performance in the natural language processing field, for sentence embedding, and generating an image by inputting the extracted features into the GAN. As a result of the experiment, the inception score was higher than that of the model used in the previous study, and when judged with the naked eye, an image that expresses the features well in the input sentence was created. In addition, even when a long sentence is input, an image that expresses the sentence well was created.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.553-556
• /
• 2003

In the field of content-based image retrieval, various mathematical low-level features have been proposed to describe the perceptual content of images. Since most of the features are assumed to be independent of each other, one feature is extracted from images without any consideration of the other features. Recently proposed CCE and SCFT taking advantage of the correlation between color and texture have shown relatively good performance. In this paper, the performance of CCE, SCFT, and the traditional regular weighted comparison method are evaluated. Simulation results with natural images have shown that CCE outperforms the other methods.

• IEIE Transactions on Smart Processing and Computing
• /
• v.1 no.3
• /
• pp.125-132
• /
• 2012

This paper proposes an automatic object segmentation and background composition method for video communication over consumer mobile phones. The object regions were extracted based on the motion and color variance of the first two frames. To combine the motion and variance information, the Euclidean distance between the motion boundary pixel and the neighboring color variance edge pixels was calculated, and the nearest edge pixel was labeled to the object boundary. The labeling results were refined using the morphology for a more accurate and natural-looking boundary. The grow-cut segmentation algorithm begins in the expanded label map, where the inner and outer boundary belongs to the foreground and background, respectively. The segmented object region and a new background image stored a priori in the mobile phone was then composed. In the background composition process, the background motion was measured using the optical-flow, and the final result was synthesized by accurately locating the object region according to the motion information. This study can be considered an extended, improved version of the existing background composition algorithm by considering motion information in a video. The proposed segmentation algorithm reduces the computational complexity significantly by choosing the minimum resolution at each segmentation step. The experimental results showed that the proposed algorithm can generate a fast, accurate and natural-looking background composition.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.875-878
• /
• 2004

Researching in text detection and segmentation has been done for a long period in the OCR area. However, there is some other area that the text detection and segmentation from images can be very useful. In this report, we first propose the design of a mobile translator system which helps non-native speakers to understand the foreign language using ubiquitous mobile network and camera mobile phones. The main focus of the paper will be the algorithm in detecting and segmenting texts embedded in the natural scenes from taken images. The image, which is captured by a camera mobile phone, is transmitted to a translator server. It is initially passed through some preprocessing processes to smooth the image as well as suppress noises. A threshold is applied to binarize the image. Afterward, an edge detection algorithm and connected component analysis are performed on the filtered image to find edges and segment the components in the image. Finally, the pre-defined layout relation constraints are utilized in order to decide which components likely to be texts in the image. A preliminary experiment was done and the system yielded a recognition rate of 94.44% on a set of 36 various natural scene images that contain texts.